Name
Abstract | ||
|---|---|---|
In this paper we consider the problem of finding a global optimum of a multimodal function applying path relinking. In particular, we target unconstrained large-scale problems and compare two variants of this methodology: the static and the evolutionary path relinking (EvoPR). Both are based on the strategy of creating trajectories of moves passing through high-quality solutions in order to incorporate their attributes to the explored solutions. Computational comparisons are performed on a test-bed of 19 global optimization functions previously reported with dimensions ranging from 50 to 1,000, totalizing 95 instances. Our results show that the EvoPR procedure is competitive with the state-of-the-art methods in terms of the average optimality gap achieved. Statistical analysis is applied to draw significant conclusions. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1007/s00500-010-0650-7 | Soft Comput. |
Keywords | Field | DocType |
large-scale problem,evopr procedure,evolutionary algorithmspath relinking � metaheuristicsglobal optimization,statistical analysis,large-scale global optimization,average optimality gap,path relinking,evolutionary path relinking,high-quality solution,explored solution,computational comparison,global optimization function | Mathematical optimization,Global optimization,Evolutionary algorithm,Computer science,Multimodal function,Global optimum,Theoretical computer science,Ranging,Artificial intelligence,Machine learning,Statistical analysis,Metaheuristic | Journal |
Volume | Issue | ISSN |
15 | 11 | 1433-7479 |
Citations | PageRank | References |
15 | 0.57 | 12 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Abraham Duarte | 1 | 418 | 31.60 |
| Rafael Martí | 2 | 1154 | 80.13 |
| Francisco Gortázar | 3 | 113 | 12.08 |